[1]} constructed a single model to train the stellar parameters and enlarge the sample to constrain the discrepancy caused by different pipelines. The uncertainty of the stellar parameter is not included in their calculation. [2]} also constructed one deep learning ANN — \(CSNet\)  — and gained stellar parameters with precision of \(\sim 55 \rm K\)  in effective temperature, 0.15 dex in log \(g\)  and 0.07 dex in [Fe/H]. [3]} presented a multi-regressior pipeline SPEEM based on three single XGB algorithm, and validated the result with SSPP. The error for each stellar parameters (\(T_{\rm eff}\) , log \(g\) , [Fe/H]) are \(41 \pm 61\)  K, \(0.11 \pm 0.17\)  dex, and \(0.09 \pm 0.14\)  dex, respectively. More information is presented in Table REF . The precision of all these single models are similar to our results.
